Copyright © 2011 Pearson Education, Inc. Publishing as Pearson Addison-Wesley
Chapter 5
More SQL: Complex Queries, Triggers,
Views, and Schema
Modification
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Chapter 5 Outline
More Complex SQL Retrieval Queries Specifying Constraints as Assertions and
Actions as Triggers Views (Virtual Tables) in SQL Schema Change Statements in SQL
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
More Complex SQL Retrieval Queries
Additional features allow users to specify more complex retrievals from database: Nested queries, joined tables, outer joins,
aggregate functions, and grouping
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Comparisons Involving NULLand Three-Valued Logic
Meanings of NULL Unknown value Unavailable or withheld value Not applicable attribute
Each individual NULL value considered to be different from every other NULL value
SQL uses a three-valued logic: TRUE, FALSE, and UNKNOWN
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Comparisons Involving NULLand Three-Valued Logic (cont’d.)
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Comparisons Involving NULLand Three-Valued Logic (cont’d.) SQL allows queries that check whether an
attribute value is NULL IS or IS NOT NULL
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Nested Queries, Tuples,and Set/Multiset Comparisons
Nested queries Complete select-from-where blocks within
WHERE clause of another query Outer query
Comparison operator IN Compares value v with a set (or multiset) of
values V Evaluates to TRUE if v is one of the elements in
V
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Nested Queries (cont’d.)
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Nested Queries (cont’d.)
Use tuples of values in comparisons Place them within parentheses
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Use other comparison operators to compare a single value v = ANY (or = SOME) operator
• Returns TRUE if the value v is equal to some value in the set V and is hence equivalent to IN
Other operators that can be combined with ANY (or SOME): >, >=, <, <=, and <>
Nested Queries (cont’d.)
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Nested Queries (cont’d.)
Avoid potential errors and ambiguities Create tuple variables (aliases) for all tables
referenced in SQL query
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Correlated Nested Queries
Correlated nested query Evaluated once for each tuple in the outer
query
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The EXISTS and UNIQUE Functions in SQL
EXISTS function Check whether the result of a correlated
nested query is empty or not
EXISTS and NOT EXISTS Typically used in conjunction with a correlated
nested query
SQL function UNIQUE(Q) Returns TRUE if there are no duplicate tuples in
the result of query Q
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Explicit Sets and Renaming of Attributes in SQL
Can use explicit set of values in WHERE clause
Use qualifier AS followed by desired new name Rename any attribute that appears in the result
of a query
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Joined Tables in SQL and Outer Joins
Joined table Permits users to specify a table resulting from
a join operation in the FROM clause of a query
The FROM clause in Q1A Contains a single joined table
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Joined Tables in SQL and Outer Joins (cont’d.)
Specify different types of join NATURAL JOIN Various types of OUTER JOIN
NATURAL JOIN on two relations R and S No join condition specified Implicit EQUIJOIN condition for each pair of
attributes with same name from R and S
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Joined Tables in SQL and Outer Joins (cont’d.)
Inner join Default type of join in a joined table Tuple is included in the result only if a matching
tuple exists in the other relation
LEFT OUTER JOIN Every tuple in left table must appear in result If no matching tuple
• Padded with NULL values for attributes of right table
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Joined Tables in SQL and Outer Joins (cont’d.)
RIGHT OUTER JOIN Every tuple in right table must appear in result If no matching tuple
• Padded with NULL values for the attributes of left table
FULL OUTER JOIN Can nest join specifications
Copyright © 2011 Ramez Elmasri and Shamkant Navathe
Aggregate Functions in SQL
Used to summarize information from multiple tuples into a single-tuple summary
Grouping Create subgroups of tuples before
summarizing
Built-in aggregate functions COUNT, SUM, MAX, MIN, and AVG
Functions can be used in the SELECT clause or in a HAVING clause
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Aggregate Functions in SQL (cont’d.)
NULL values discarded when aggregate functions are applied to a particular column
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Grouping: The GROUP BY and HAVING Clauses
Partition relation into subsets of tuples Based on grouping attribute(s) Apply function to each such group
independently
GROUP BY clause Specifies grouping attributes
If NULLs exist in grouping attribute Separate group created for all tuples with a
NULL value in grouping attribute
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Grouping: The GROUP BY and HAVING Clauses (cont’d.)
HAVING clause Provides a condition on the summary
information
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Discussion and Summary of SQL Queries
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Specifying Constraints as Assertions and Actions as
Triggers CREATE ASSERTION
Specify additional types of constraints outside scope of built-in relational model constraints
CREATE TRIGGER Specify automatic actions that database
system will perform when certain events and conditions occur
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Specifying General Constraints as Assertions in SQL
CREATE ASSERTION Specify a query that selects any tuples that
violate the desired condition Use only in cases where it is not possible to
use CHECK on attributes and domains
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Introduction to Triggers in SQL
CREATE TRIGGER statement Used to monitor the database
Typical trigger has three components: Event(s) Condition Action
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Views (Virtual Tables) in SQL
Concept of a view in SQL Single table derived from other tables Considered to be a virtual table
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Specification of Views in SQL
CREATE VIEW command Give table name, list of attribute names, and a
query to specify the contents of the view
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Specification of Views in SQL (cont’d.)
Specify SQL queries on a view View always up-to-date
Responsibility of the DBMS and not the user
DROP VIEW command Dispose of a view
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View Implementation, View Update, and Inline Views
Complex problem of efficiently implementing a view for querying
Query modification approach Modify view query into a query on underlying
base tables Disadvantage: inefficient for views defined via
complex queries that are time-consuming to execute
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View Implementation
View materialization approach Physically create a temporary view table when
the view is first queried Keep that table on the assumption that other
queries on the view will follow Requires efficient strategy for automatically
updating the view table when the base tables are updated
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View Implementation (cont’d.)
Incremental update strategies DBMS determines what new tuples must be
inserted, deleted, or modified in a materialized view table
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View Update and Inline Views
Update on a view defined on a single table without any aggregate functions Can be mapped to an update on underlying
base table
View involving joins Often not possible for DBMS to determine
which of the updates is intended
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Clause WITH CHECK OPTION Must be added at the end of the view definition
if a view is to be updated
In-line view Defined in the FROM clause of an SQL query
View Update and Inline Views (cont’d.)
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Schema Change Statements in SQL
Schema evolution commands Can be done while the database is operational Does not require recompilation of the database
schema
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The DROP Command
DROP command Used to drop named schema elements, such
as tables, domains, or constraint
Drop behavior options: CASCADE and RESTRICT
Example: DROP SCHEMA COMPANY CASCADE;
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The ALTER Command
Alter table actions include: Adding or dropping a column (attribute) Changing a column definition Adding or dropping table constraints
Example: ALTER TABLE COMPANY.EMPLOYEE ADD COLUMN Job VARCHAR(12);
To drop a column Choose either CASCADE or RESTRICT
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The ALTER Command (cont’d.)
Change constraints specified on a table Add or drop a named constraint
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Summary
Complex SQL: Nested queries, joined tables, outer joins,
aggregate functions, grouping
CREATE ASSERTION and CREATE TRIGGER
Views Virtual or derived tables